A SCALE AND ROTATION INVARIANT FAST IMAGE MINING FOR SHAPES

In recent times, fast content - based image retrieval is required in image mining for shapes, as image database is rising exponentially in size with time. In this paper, a sample, fast and efficient process through Distance Mapping for CBIR is proposed. In distance mapped domain, zero valued points represent the boundary of the object and the rest are assigned the value with the shortest distance to the boundary of the object. All the regional points within the object are defined with positive distance while the background points are given negative distances. The shape signature proposed is obtained from the statistical properties of the distance mapped functional. The features used are the number of grid points within pre-specified narrow band region across the object boundary and their algebraic relationship. It is found that, these features are invariant to sealing and rotations. T his increases the retrieval rate and also the speed of retrieval as no pre-scaling and rotation are required to register the shape. Conventional distance mapping is a time consuming process. This process can be used as coarse level in hierarchical CBIR that shrinks the database size from large set to a small one. This tiny database can further the database can be scrutinized meticulously using the above said methods like wavelets, curve lets etc. For feature extraction to improve the retriveval rate or even in the present work using accurate distance mapping.